Details
| Original language | English |
|---|---|
| Pages (from-to) | 563-571 |
| Number of pages | 9 |
| Journal | Production Engineering |
| Volume | 15 |
| Issue number | 3-4 |
| Early online date | 11 Mar 2021 |
| Publication status | Published - Jun 2021 |
Abstract
Self-optimizing process planning is an essential approach for finding optimum process parameters and reducing ramp-up times in machining processes. For this purpose, polishing is presented as an application example. In conventional polishing processes, the process parameters are selected according to the operator’s expertise in order to achieve a high-quality surface in the final production step. By implementing machine learning (ML) models in process planning, a correlation between process parameter and measured surface quality is generated. The application of this knowledge automates the selection of optimal process parameters in computer-aided manufacturing (CAM) and enables a continuous adaptation of the NC-code to changing process conditions. Applying the presented ML-model, the prediction accuracy of 83% will adapt the process parameters to achieve the target roughness of 0.2 μm. The sample efficiency is shown by the decrease in root mean square error from 0.1–0.28 to 0.02–0.07 μm with additional polishing iterations.
Keywords
- Abrasive polishing, Machine learning, Optimization, Process planning, Simulation-based planning
ASJC Scopus subject areas
- Engineering(all)
- Mechanical Engineering
- Engineering(all)
- Industrial and Manufacturing Engineering
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In: Production Engineering, Vol. 15, No. 3-4, 06.2021, p. 563-571.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Self-optimizing process planning of multi-step polishing processes
AU - Denkena, Berend
AU - Dittrich, Marc André
AU - Nguyen, Hai Nam
AU - Bild, Konrad
N1 - Funding Information: This research has been funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy within the Cluster of Excellence PhoenixD (EXC 2122, Project ID 390833453).
PY - 2021/6
Y1 - 2021/6
N2 - Self-optimizing process planning is an essential approach for finding optimum process parameters and reducing ramp-up times in machining processes. For this purpose, polishing is presented as an application example. In conventional polishing processes, the process parameters are selected according to the operator’s expertise in order to achieve a high-quality surface in the final production step. By implementing machine learning (ML) models in process planning, a correlation between process parameter and measured surface quality is generated. The application of this knowledge automates the selection of optimal process parameters in computer-aided manufacturing (CAM) and enables a continuous adaptation of the NC-code to changing process conditions. Applying the presented ML-model, the prediction accuracy of 83% will adapt the process parameters to achieve the target roughness of 0.2 μm. The sample efficiency is shown by the decrease in root mean square error from 0.1–0.28 to 0.02–0.07 μm with additional polishing iterations.
AB - Self-optimizing process planning is an essential approach for finding optimum process parameters and reducing ramp-up times in machining processes. For this purpose, polishing is presented as an application example. In conventional polishing processes, the process parameters are selected according to the operator’s expertise in order to achieve a high-quality surface in the final production step. By implementing machine learning (ML) models in process planning, a correlation between process parameter and measured surface quality is generated. The application of this knowledge automates the selection of optimal process parameters in computer-aided manufacturing (CAM) and enables a continuous adaptation of the NC-code to changing process conditions. Applying the presented ML-model, the prediction accuracy of 83% will adapt the process parameters to achieve the target roughness of 0.2 μm. The sample efficiency is shown by the decrease in root mean square error from 0.1–0.28 to 0.02–0.07 μm with additional polishing iterations.
KW - Abrasive polishing
KW - Machine learning
KW - Optimization
KW - Process planning
KW - Simulation-based planning
UR - http://www.scopus.com/inward/record.url?scp=85102488967&partnerID=8YFLogxK
U2 - 10.1007/s11740-021-01042-6
DO - 10.1007/s11740-021-01042-6
M3 - Article
AN - SCOPUS:85102488967
VL - 15
SP - 563
EP - 571
JO - Production Engineering
JF - Production Engineering
SN - 0944-6524
IS - 3-4
ER -